arXiv:2604.27660v3 Announce Type: replace Abstract: Many real-world tasks require language models (LMs) to reason over complex contexts that exceed their parametric knowledge. This calls for context learning, where LMs directly learn relevant knowledge from the given context. An intuitive solution is inference-time skill augmentation: extracting the rules and procedures from context into natural-language skills. However, constructing such skills for context learning scenarios faces two challenges: the prohibitive cost of manual skill annotation for long, technically dense contexts, and the lac
The proliferation of advanced language models necessitates new methods for them to efficiently learn and apply knowledge from increasingly complex and voluminous contexts, moving beyond mere parametric recall.
This research explores a critical limitation of current language models concerning their context learning capabilities, directly impacting their ability to perform complex real-world tasks effectively and autonomously.
The proposed 'inference-time skill augmentation' method fundamentally alters how LMs might acquire and utilise knowledge from given contexts, moving towards more dynamic and flexible reasoning.
- · AI agents developers
- · Generative AI platforms
- · Large language model researchers
- · Systems relying solely on pre-trained parametric knowledge
- · Manual data annotation services for complex contexts
Language models will become more adaptable and powerful in handling novel and complex information without needing extensive retraining.
This improved context learning could accelerate the development and deployment of more sophisticated AI agents capable of autonomous decision-making in diverse environments.
Enhanced skill learning from context might reduce the cost and technical barrier for deploying custom AI solutions, democratizing access to advanced AI capabilities.
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Read at arXiv cs.AI